Electronic Journal of Structural Engineering

Volume 26, Issue 2 (2026) of the Electronic Journal of Structural Engineering brings together eleven original research articles that reflect current developments in structural engineering, with particular emphasis on resilient infrastructure, sustainable construction materials, and the integration of advanced computational techniques into engineering practice. The contributions address a broad spectrum of challenges spanning earthquake engineering, structural systems, material performance, and intelligent data-driven analysis.

A prominent theme throughout this issue is the enhancement of structural resilience under seismic loading. The collection includes investigations into the seismic performance of prefabricated concrete frame nodes, retrofitting strategies for reinforced concrete bridges, the coupled dynamic response of multi-story suspended floor systems considering soil–structure interaction, and the structural behavior of concentrically braced frames incorporating buckling-restrained braces and steel shear wall systems. Together, these studies provide valuable insights into the design and assessment of structural systems capable of withstanding complex loading conditions.

The issue also highlights ongoing advances in sustainable construction materials through experimental investigations into cement mortars incorporating recycled plastic waste and concrete slabs produced with coal gasification ash slag. These contributions demonstrate the continued pursuit of environmentally responsible material solutions while maintaining or enhancing structural performance.

Complementing the experimental and numerical studies is a strong focus on the application of artificial intelligence and optimization methodologies in structural engineering. Featured contributions include a comprehensive review of deep learning architectures for automated crack detection, machine learning approaches for predicting the punching resistance and failure modes of slab–column connections, and intelligent optimization frameworks for the analysis and design of coal mine roadway support structures. These studies illustrate the growing role of data-driven techniques in improving predictive capability, design efficiency, and engineering decision-making.

Collectively, the articles presented in this issue underscore the interdisciplinary nature of contemporary structural engineering research, demonstrating how experimental investigation, advanced numerical modeling, and intelligent computational methods can be integrated to enhance the safety, resilience, sustainability, and performance of civil infrastructure.